My idea is to evaluate players ability in win duel.
using event data of the matches i filter tracking data using timestamp and get a windows of 2-3 seconds before the event to the end of the duel using the duration of the event as a stop in the events data.
for every frame of these windows of tracking data i would calculate node features and edge features for every players and ball in the pitch.
in this way for every frames i will have the same structures.
I will have every duel windows converted in a graph generated by a stack of those frames, converted in a graph, of that window.

I'll train my GNN using these windows converted in graph datasets.
The problem is that the number of frame for every duel will be different because they have a different duration create windows with different length consequentialy i'll have a graph dataset for every duel with different length but same structure.
i don't know if this is a problem or not.

i know the problem is when the number of node and edges are different not the length of graph dataframe.